Exploration of TPUs for AI Applications
It addresses the need for efficient AI acceleration to save time, money, and environmental resources, but is incremental as it focuses on overview and comparative analysis.
This paper explores Tensor Processing Units (TPUs) for AI applications in cloud and edge computing, finding that TPUs provide significant performance improvements compared to other chip architectures.
Tensor Processing Units (TPUs) are specialized hardware accelerators for deep learning developed by Google. This paper aims to explore TPUs in cloud and edge computing focusing on its applications in AI. We provide an overview of TPUs, their general architecture, specifically their design in relation to neural networks, compilation techniques and supporting frameworks. Furthermore, we provide a comparative analysis of Cloud and Edge TPU performance against other counterpart chip architectures. Our results show that TPUs can provide significant performance improvements in both cloud and edge computing. Additionally, this paper underscores the imperative need for further research in optimization techniques for efficient deployment of AI architectures on the Edge TPU and benchmarking standards for a more robust comparative analysis in edge computing scenarios. The primary motivation behind this push for research is that efficient AI acceleration, facilitated by TPUs, can lead to substantial savings in terms of time, money, and environmental resources.